national minimum wage in south africa: quantification of...

National Minimum Wage in South Africa:

Quantification of Impact

Asghar Adelzadeh, Ph.D.

Director and Chief Economic Modeller Applied Development Research Solutions (ADRS)

([email protected])

Cynthia Alvillar, MA, JD CEO and Senior Labour Market Specialist

Applied Development Research Solutions (ADRS) [email protected]

Presentation to:

National Minimum Wage Symposium University of Witwatersrand Johannesburg, South Africa

2-3 February 2016

mailto:[email protected]






Objective

To use economic modelling techniques to quantify the potential impact of introducing a National Minimum Wage (NMW) in South Africa.

Outline

I. The ADRS Dynamically Integrated Macro-

Micro Simulation Model of South Africa

(DIMMSIM)

I. Scenarios for NMW

II. Data sources and preparation

IV-IX. Model simulation results: Macroeconomic, industry, poverty and inequality impact

X. Conclusions

I. THE ADRS DYNAMICALLY INTEGRATED MACRO-MICRO SIMULATION MODEL OF SOUTH AFRICA (DIMMSIM)

DIMMSIM is a linked macro-micro model that captures the interactions between the macroeconomy and household poverty and income inequality in South Africa.

Its macro model component is based on the ADRS Macroeconometric Model of South Africa (MEMSA).

Its micro model component is based on the ADRS South African Tax and Transfer Simulation Model (SATTSIM).

Overview of DIMMSIM

It is designed to capture the structure, complexity and dynamics of the South African economy.

The key to understand DIMMSIM’s projections of the impact of the NMW policy is to understand how it captures production, expenditure, and distribution sides of the economy and their dynamic interactions based on time series analysis of bottom up representation of each side.

Distinctive features of MEMSA

Production, Expenditure and Distribution

GDP From Production Side: MEMSA represents the South African economy from its production side, where the size of the economy chiefly reflects the contributions of its 4 primary, 28 manufacturing, and 9 service sectors.

Time series analysis of determinants of short and long term dynamics of 41 Sector output

45 Sector prices


GDP From Expenditure Side: It also represents the economy from the expenditure perspective: that is, the total output of the economy reflects the sum of total demand for domestically produced goods and services. This includes households and government final consumption expenditures, private sector and government investment expenditures, net exports of goods and services and changes in the inventory. Time series analysis of determinants of short and long term dynamics

of 22 categories of household consumption expenditures

41 sector investment

41 sector export

38 sector imports


GDP From Distribution Side: Finally, the model captures the economic pie in terms of its distribution between the main actors in the economy in the form of wages and salaries (remuneration), profits (gross operating surplus), and government net revenue. The relative share of the main actors in the economy reflects each group’s claim on the economy. Time series analysis of determinants of short and long term dynamics

of 45 sector average real wage rate

41 sector demand for employment

Government share based on application of tax and subsidy rates on production and products

MEMSA ‘s bottom up structure consists of more than 3200 equations and more

than 400 behavioural equations. Utilises modern time series estimation methods to build the model’s system of

equations. Built on broad pluralistic theoretical foundations and relevant empirical literature. The equations capture the structure of the National Income and Product Account

(NIPA) in a highly disaggregated manner that includes 7 estimated variables for 41 economic sectors. The model includes:

45 categories of investment 45 categories of employment 45 categories of average remuneration rates 45 categories of outputs 45 categories of exports 45 categories of imports 103 categories of prices 26 categories of private consumption expenditure 16 categories of private sector’s income and expenditure 16 categories of households income and expenditure 28 categories of government sector income and expenditure

Distinctive features of MEMSA

1. Agriculture, Forestry and Fishing

2. Coal Mining3. Gold, uranium and ore

mining4. Other mining

5. Food

6. Beverage

7. Tobacco

8. Textiles

9. Wearing Apparel

10. Leather and Leather products

11. Footwear

12. Wood and wood products

13. Paper and paper products

14. Printing, publishing and recorded media

15. Coke & refined petroleum products

16. Basic chemicals

17. Other chemicals & man made fibres

18. Rubber products

19. Plastic products

20. Glass and glass products

21. Non-metalic minerals

22. Basic iron & steel

23. Basic non-ferrous metals

24. Metal products excl.machinery

25. Machinery and equipment

26. Electrical equipment

27. Tv, radio & communication equipment

28. Professional & scientific equipment

29. Motor vehicles, parts & accessories

30. Other transport equipment

31. Furniture

32. Other industries

Primary Manufacturing Services

MEMSA's Economic Sectors7 variables for each sector: output, employment, investment, exports, imports, prices, wage rates

33. Electricity, Gas and water

34. Building construction and engineering

35. Wholesale, retail trade, catering &

accomodation services

36. Transport, storage, and communication

37. Financial services, business intermediation,

insurance & real estate

38. Community, social & personal services

39. Other services

40. Households

41. General government

Aggregate Sectors

42. Total primary (sum of sectors 1 to 4)43. Total manufacturing (sum of sectors 5 to 32)

44. Total services (sum of sectors 33 to 41)

45. Total economy (sum of sectors 1 to 41)

Distinctive features of SATTSIM

The ADRS South African Tax and Transfer

Simulation Model (SATTSIM) is the microeconomic model underlying DIMMSIM.

SATTSIM is a full microsimulation model.

By linking government tax and transfer policies to individuals, families and households it can facilitate simulation of eligibility, budgetary, poverty and distribution impact of changes in direct and indirect taxes, social security and public works programmes.

Distinctive features of SATTSIM

Database of detailed demographic, work, income and expenditure

information of 30,000 households made up of 62,000 families and about 125,000 individuals.

Database of policy parameters related to government tax, social security and EPWP policies and programmes.

Two tax modules that use computer codes to parameterise and capture the details of current income tax and indirect tax policies.

Eleven social security and public works modules use computer codes to parameterise and capture eligibility and entitlement conditions of government social security programmes (e.g., child support, disability grant, etc.), several grant programmes (e.g., basic income grant, care giver grant, etc.), and the expanded public works programme (EPWP).

Modules impute receipt of social security, tax liability, poverty and income inequality

Modules produce aggregate and cross tabulation of results by gender, race, province, family type, locality and quintile.

Interaction between DIMMSIM macro and micro models

The model’s computer programme transmits macro model results (e.g.,

prices, wages, employment) to the microsimulation component and transmits microsimulation results (e.g., total taxes, total government transfers, etc.) to the macro model.

Model solutions are consistent between macro and micro models in terms of government transfers to and income from households, direct and indirect taxes, and other variables that link the two models.

DIMMSIM’s two-way macro-micro links

Macro model

Transmits: prices,

wages, employment

Household microsimulation model

Transmits: Total income and

indirect taxes, total

government transfers, etc.)

Dynamically Integrated Macro-Micro Simulation Model (DIMMSIM)

Final Demand Blocks

Private Household

Consumption

Public + Private Investment

Government

Consumption

Output Blocks

GVA at basic prices

GVA at Market Prices

GDP at Factor Cost

Employment Block

Financial Block

Monetary Policy

Interest rates

Exchange rates

Money supply

Credit

Wealth

Debt

Long Term Blocks

Income/Expend/Savings

Blocks

Exports

Imports

Prices/Wages Blocks

Wage rates

Sector prices

Consumption deflators

Investment deflators

GDP deflator

Consumer Price Index

Producer Price Index

Inventory

Microsimulation Modules

Income Tax and Indirect Taxes

Old Age Pension

Child Support Grant

Disability Grant

Care Dependency Grant

Care Giver Grant

Basic Income Grant

Income/Expenditure/Saving

Poverty

Income Distribution

Output

Accounting Consistency

Blocks

Primary sector

Secondary sector

Tertiary sector

Households

Business

Government

(Fiscal policy)

Macroeconomic

Microeconomic

Linked Macro-Micro

Exogenous and Parameter

Block

Population

Oil price

Gold price

OECD Growth Rate

Sub-Sahara Growth Rate

U.S. Interest Rate

Import prices

Policy variables

Policy parameters

Other variables

Investment

Consumption

Employment

Exports & Imports

Wage rate/Prices/Deflators

Source: Adelzadeh, A.. Applied Development Research Solutions (ADRS), www.adrs-global.com

DIMMSIM National Minimum Wage Module

Facilitates the design and simulation of various formulations of the national minimum wage (NMW) for South Africa.

Estimates and transmits the magnitudes of annual shocks to the macro model’s economic sector’s average real remuneration rates due to the introduction of alternative NMW scenarios.

DIMMSIM National Minimum Wage Module

Accommodates temporary or permanent sectoral exemptions, annual variations/adjustments to the NMW, and the introduction of NMW as a flat rate or indexed form.

For each scenario, adjusts the wage income of existing full time employees whose wage rates are below the scenario’s NMW rate.

II. SCENARIOS OF THE NATIONAL MINIMUM WAGE

NMW Policy Scenarios

Objectives: to quantify the likely impact of alternative NMW policies for the South African economy.

Five Scenarios: One base scenario and four NMW scenarios

No NMW: Base Scenario

The Base Scenario captures the economy ‘as it is’ with no NMW. It reflects ‘what if’ economic performance continues its current low growth and employment path. Key features of the Base Scenario: Fiscal Policy: Captures Treasury’s current concern about the Debt-GDP ratio and sets low annual targets for the deficit-GDP ratio. Thus, the Base Scenario strives to achieve a balanced or close to balanced annual budget. Monetary Policy: Adheres to government’s current inflation target policy and assumes that the policy will remain unchanged over the next 5 years. For the model, this means that monetary authorities will use the interest rate to keep inflation within the 3 to 6 percentage target band.


Public Investment: Nominal investment by general government and public corporations is designed to increase by 6% annually during the projection period.

Government Final Consumption (GFC) Expenditure: GFC is expected to grow by 6.2% annually in nominal terms, which corresponds to the MTEF’s current average annual rate.

International Outlook: Assumes that average real annual growth rate for the OECD and Sub-Saharan countries will be 1% and 5% respectively, over the next 10 years. The price of a barrel of crude oil is set to gradually increase to 70 US Dollar by 2025.


Taxes, Social Grants and EPWP: The scenario assumes that all nominal parameters related to direct and indirect taxes, social grants and EPWP (e.g., tax brackets, grant amounts) increase by 6 percent annually during the projection period.

Poverty Line: The scenario adopts poverty line of R680 per capita and R930 per adult equivalent per month for 2015. Both poverty lines are adjusted by 6 percent annually.

NMW: A Minimal Scenario

The Minimal Scenario expands coverage of the minimum wage without increasing labour costs for the majority of firms. Thus, it sets a NMW near the level of the lowest sectoral determinations. Key features: Sets NMW for 2016 at R2250 per month, which is basically the same as adjusting the lowest sectoral wage determinations in 2015 by inflation (6 percent). NMW is annually adjusted for inflation after 2016.

NMW: Indexed 40% Scenario

This scenario progressively increases the value of the NMW relative to an index. It reflects the OECD norm of establishing a minimum wage that corresponds to the mean wage.

Key features:

NMW is indexed to the inflation adjusted average wage rate of full time workers. In 2016 NMW is indexed to 40% of the 2015 mean wage for all full time workers, or R3467. The index is annually increased by 1% until it reaches 45% of the inflation adjusted 2015 mean wage rate by 2021. After 2021, the NMW annually adjusts for inflation with the index kept at 45%. For three very low-wage sectors, different rates are set as a percentage of the NMW for each year:

Agriculture minimum wage is set to 80% of the NMW. Domestic workers and EPWP minimum wage rates are set at at 70% of the NMW.

NMW: Indexed 45% Scenario

This scenario targets the living standards of a larger portion of workers.

Key features:

NMW is indexed to the inflation adjusted average wage rate of full time formal sector workers.

In 2016 NMW is indexed to 45% of the 2015 mean wage of full time formal sector workers, excluding agriculture and domestic work, or R4623.

The index increases annually by 1% until it reaches 50% of the inflation adjusted 2015 mean wage rate by 2021.

After 2021, the NMW annually adjusts for inflation with the index kept at 50%.

For three very low-wage sectors, different rates are set as a percentage of the NMW for each year. For agriculture, the rate is set to 80% of the NMW. For domestic workers and the EPWP the rate is set at 70% of the NMW.

NMW: A Maximal Scenario

This scenario captures the transformation of South Africa’s wage structure in a far-reaching manner by using a higher starting minimum wage. Key features: NMW begins in 2016 at R6000 to ensure that 65% of full-time workers are covered by the measure. It annually adjusted for inflation plus 2% until 2021. After 2021, the NMW annually adjusts for inflation. For three very low-wage sectors, different rates are set as a percentage of the national minimum wage each year. For agriculture, the rate is set to 80% of the NMW. For domestic work the rate is pegged at 70% of the NMW. For the EPWP the rate is set at 60% of the NMW.

III. NMW DATA SOURCES AND PREPARATION

Breakdown of Sector Employment

QLFS 2014 data was used to break down sector employment into 8 categories. Each sector employment category is created in relation to the mean wage rate for the sector: less than 25% of the mean, between 25% and 40% of the mean, between 40% and 50% of the mean, between 50% and 75% of the mean, between 75% and 100% of the mean, between 100% and 150% of the mean, between 150% and 200% of the mean, greater than 200% of the mean.

Each category includes the number of workers within that category and a corresponding mean wage rate.

The prepared data excludes self-employed and part time workers.

Simulations of NMW Policy Scenarios

DIMMSIM used specifications of each policy scenario to simulate impact on:

Economic indicators at macroeconomic and sector levels

Household inequality and poverty

IV. MODEL SIMULATION RESULTS WAGE RATES

Key Finding: After 2016, nominal NMW rates increase between 6% and 8.42%, average annually

6000

11994

4623

9373

3267

6762

2250

3801

0

14000

2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

NMW Scenarios: National(Nominal, Rand per month, 2016-2025)

Minimal Maximal

Index 40% Index 45%

2250

38014800

9595

2614

5409

3699

7499

0

14000

2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

NMW Scenarios: Agriculture

Minimal Maximal

Index 40% Index 45%

2250

38014200

8396

2287

47333236

6561

0

14000

2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

Source: DIMMSIM, WWW.ADRS-Global.com

NMW Scenarios: Domestic Workers

Minimal Maximal

Index 40% Index 45%

2250

38013600

7196

2287

47333236

6561

0

14000

2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

NMW Scenarios: EPWP

Minimal Maximal

Index 40% Index 45%

Key Finding: After 2016, real NMW rates increase between 0% and 2.28%, average annually

2250 2250

6000

7099

32674002

4623

5548

0

8000

2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

NMW Scenarios: National(Rand per month, Constant 2016 Prices, 2016-2025)

2250 2250

48005679

26143202

36994438

0

8000

2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

NMW Scenarios: Agriculture

Minimal Maximal

Index 40% Index 45%

22502250

42004969

22872802

32363884

0

8000

2016 2017 2018 2019 2020 2021 2022 2023 2024 2025


NMW Scenarios: Domestic Workers

22502250

36004260

2287 28023236

3884

0

8000

2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

NMW Scenarios: EPWP

Minimal Maximal

Index 40% Index 45%

Key Finding: NMW raises the average real wage rate across sectors

7,7

26

14

,02

4

6,6

54

7,6

17

8,1

66

14

,07

3

7,4

87

8,0

37

9,0

55

14

,38

1

8,9

48

8,7

67

9,9

73

15

,10

7

9,5

08

9,7

61

8,2

29

15

,55

2

9,0

30

8,9

08

-

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

Primary Manufacturing Services Total economy

( R

and

, Co

nst

ant

20

10

pri

ces)

Source: DIMMSIM, www.ADRS-Global.com)

NMW and Remuneration (Average monthly, 2016-2025)

Base Minimal Index (40%) Index (45%) Maximal

V. MODEL SIMULATION RESULTS: INCOME AND EXPENDITURE

Key Finding: NMW raises household income

3.33 3.49

4.64 4.57

6.64

0

1

2

3

4

5

6

7

BAU Minimal Index 40% Index 45% Maximal

( %

)

Source: DIMMSIM, www.ADRS-Global.com

Real Household Income (Avg. Annual Growth Rate, 2016-2025)

Key Finding: NMW reduces demand for social grants

-0.97

-3.06

-7.40

-10.38

-12

-10

-8

-6

-4

-2

0

Minimal Index 40% Index 45% Maximal

( %

)


NMW and Demand for Social Security (2025) (% change relative to BAU)

Key Finding: NMW increases direct and indirect tax revenue

0.83

0.54

1.53

1.00

2.28

1.60

3.69

2.49

0

1

2

3

4

Income Tax Indirect tax

( %

)


NMW and Tax Revenue from Households (2025) (% difference relative to BAU)

Minimal Index 40%

Index 45% Maximal

Key Finding: NMW raises household Consumption Expenditure

1,700,000

1,900,000

2,100,000

2,300,000

2,500,000

2,700,000

2,900,000

2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

(R m

illio

ns,

20

10

Pri

ces)


NMW and Household Consumption Expenditure (2015-2025)


Key Finding: NMW increases household consumption expenditure

4.0

0.9

1.8 1.8 2.0

4.2

1.4

2.0 2.2 2.3

5.0

2.1

2.9 3.1 3.2

5.2

2.4

3.0 3.2 3.3

6.3

3.4

4.0 3.6

4.1

0

1

2

3

4

5

6

7

Durable Goods Semi-Durable Goods Non-Durable Goods Services Total

( %

)

Source: DIMMSIM, www.adrs-global.com

Household Real Consumption Expenditure (Avg. annual growth rate, 2015-2025)


23.0522.78

22.1121.86 21.81

18

19

20

21

22

23

24

25


(In

vest

men

t-G

DP

Ra

tio

, %)


Investment Share (2016-2025)(Avgerage annual)

19%

20%

21%

22%

23%

24%

25%

26%

2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

(In

ves

tme

nt

rela

tive

to

G

DP

)

Investment Trends (2016-2025)


5.1 5.1

4.7 4.64.9

2

3

4

5

6


(CA

GR

, %

)

Total Investment Expenditure(Avg. annual growth rate, 2016-2025)

0.7

2.42.6

3.8

0.8

2.5

3.1

4.3

-0.1 -0.2-0.6 -0.6

-1.0

0.0

1.0

2.0

3.0

4.0

5.0


( %

)


Output, Capital and Labour Ratios(% change relative to BAU, Avg. annual 2016-2025)

Labour Productivity

Capital Productivity

Capital Labour Ratio

Key Finding: NMW leads to higher exports and imports

1.14

1.65

1.94

2.50 2.47

1.75 1.99

2.49 2.64

3.45

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0


( %

)


NMW and Real Exports and Imports (Avg. annual growth rate, 2016-2025)

Exports Imports

VI. MODEL SIMULATION RESULTS: PRODUCTION AND GROWTH

3,000,000

3,200,000

3,400,000

3,600,000

3,800,000

4,000,000

4,200,000

4,400,000

2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

(R m

illio

ns,

20

10

Pri

ces)


NMW and Gross Domestic Product (GDP)


2.42 2.52 2.82 2.91

3.26

0

1

1

2

2

3

3

4


( %

)

NMW and Real GDP Growth (Avg. Annual)

Key Finding: NMW is pro-growth

Key Finding: NMW enhances sector growth

0.9

5.1

-0.2 0.7

3.9

12.8

-0.2

2.1

4.5

14.5

-0.1

2.1

6.3

18.9

0.9

4.1

-1

2

5

8

11

14

17

20


( %

)


NMW and Economic Output (2016-2025) (% difference relative to BAU, Avg. annual)

Minimal Index 40%

Index 45% Maximal

VII. MODEL SIMULATION RESULTS: EMPLOYMENT

Key Finding: NMW affects sector employment differently

0.5

3.4

-0.4 0.0

1.9

7.0

-1.2 -0.3

3.1

7.2

-1.1 -0.3

4.0

8.3

-0.9 0.2

-2.0

0.0

2.0

4.0

6.0

8.0

10.0


( %

)


NMW and Employment (2016-2025) (% difference relative to BAU, Avg. annual)

Minimal Index 40%

Index 45% Maximal

Key Finding: NMW marginally increases the unemployment rate

25.2 25.2 25.4 25.3 25.1

20

22

24

26

28


(%)


NMW and Unemployment Rate (2016-2025) (Ave. Annual)

VIII. MODEL SIMULATION RESULTS: PRODUCTIVITY, INFLATION AND DEBT

Key Finding: NMW increases productivity that helps producers become less dependent on using capital to expand output

0.7

2.4 2.6

3.8

0.8

2.5

3.1

4.3

-0.1 -0.2

-0.6 -0.6 -1.0

0.0

1.0

2.0

3.0

4.0

5.0


( %

)


Output, Capital and Labour Ratios (% change relative to BAU, Avg. annual 2016-2025)

Labour Productivity Capital Productivity

Capital Labour Ratio

Key Finding: NMW will be accompanied with stable and low inflation rate

6.4 6.4

6.1

5.7

5.9

5.0

5.5

6.0

6.5

7.0


(%)


NMW and Inflation Rate (Ave. Annual, 2016-2025)

Key Finding: The Debt-GDP ratio will be sustainable

44.6

41.9

45.9

40.1

46.1

30

34

38

42

46

50


(%)


NMW and Debt-GDP Ratio (Ave annual, 2016-2025)

IX. MODEL SIMULATION RESULTS: INEQUALITY AND POVERTY

Key Finding: NMW reduces income inequality

66.4 65.9 65.6 64.8 64.2

0

20

40

60

80

100


(Gin

i In

dex

, %)


NMW and Income Inequality (2025)

Key Finding: NMW reduces poverty

29.3

28.2

27.5

26.9 26.7

20

22

24

26

28

30


( %

)


NMW and Poverty Headcounts (2025)

Key Finding: NMW reduces rural poverty relatively more

22.6 22.0 21.8 21.6 21.5

37.8 35.9

34.8 33.7 33.3

0

5

10

15

20

25

30

35

40


( %

)


NMW and Poverty by Location (2025)

Urban Rural

Key Finding: NMW reduces both male and female poverty rates

27.4 26.2 25.6 25.0 24.7

31.2 30.0 29.4 28.8 28.6

0

5

10

15

20

25

30

35


( %

)


NMW and Poverty by Gender (2025)

Male Female

Key Finding: NMW reduces poverty rate among the bottom 80% of population, specially the bottom 20%

60.6

56.6 54.0

51.3 50.4

38.6 37.5 36.8 36.2 35.9

25.4 24.8 24.7 24.6 24.6

19.4 19.1 19.0 19.0 19.0

0

10

20

30

40

50

60

70


( %

)


NMW and Poverty by Quintile (2025)

Quintile 1 (bottom) Quintile 2 Quintile 3 Quintile 4

X. CONCLUSIONS

Conclusions

Overall, a NMW in South Africa is found to be a pro-poor measure that reduces poverty and inequality and improves economic growth.

The suitable approach to measure the potential impact of the NMW takes account of dynamic interactions between expenditure, production and distribution.

The introduction of the NMW and the subsequent wage adjustments of 48 percent and more full time workers across sectors of the economy help increase the average sector wage rates from below. This contrasts with historical trends of increases in sector average wage rates resulting from disproportionate increases in the remuneration of high skilled workers.

Similarly, the contribution of a NMW to the increase in total household income mainly reflects its positive impact on the income of the working poor and not through an increase in income of high income earners. This distinction between the BAU scenario and the NMW scenarios has significant implications for the evolution of poverty and inequality over the next 10 years.

Conclusions

The model results show that the introduction of a NMW will predominantly have positive impact on key macroeconomic and industry indicators.

At the same time, the introduction of a NMW is projected to significantly reduce headcount poverty, specially among the bottom quintile, and to reduce income inequality.

Its projected positive impact on households income and expenditure is also projected to reduce demand for social grants and increase government revenue from income and value added taxes

The negative effects of introducing a meaningful NMW on some economic indicators do not threaten macroeconomic balance. They may therefore be considered acceptable trade offs for a policy with significant positive contributions to household real disposable income and economic growth.

The projected positive contribution of a NMW to economic growth indicates that it can help the economy break away from the current vicious cycle of a low growth path

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